HYDRAULIC FRACTURING CANDIDATE-WELL SELECTION USING ARTIFICIAL INTELLIGENCE APPROACH - Agus Aryanto; Sugiatmo Kasmungin; Fathaddin M. T. (Lembaga Penelitian Universitas Trisakti, 2017)
Hydraulic fracturing is one of the stimulation method that aimed to increaseproductivity of well by creating a high conductive conduit in reservoir connecting it to thewellbore. This high conductivity zone is created by injecting fluid into matrix formation withenough rate and pressure. After crack initiate and propagate, the process continue withpumping slurry consist of fracturing fluid and sand. This slurry continues to extend thefracture and concurrently carries sand deeply into formation. After the materials pumped,carrier fluid will leak off to the formation and leave the sand holds the fracture created.TLS Formation in X and Y Field is widely known as a formation that have lowproductivity since it has low permeability around 5 md and low resistivity 3 Ohm-m. Oilfrom TLS formation could not be produced without fracturing. This formation also havehigh clay content, 20 – 40 % clay. Mineralogy analysis also shown that this formationcontains water sensitive clay such as smectite and kaolinite. Hydraulic fracturing has beendone in this field since 2002 on around 130 wells.At the beginning of hydraulic fracturing campaign, the success parameter is only tomake the wells produce hydrocarbon in economical rate. As the fractured wells becomelarger in number, several optimization is also been done to increase oil gain. Later on, theneeds of conclusive analysis to evaluate well performance after hydraulic fracturing riseup due to sharp decrement of crude oil price. Accurate analysis and recommendationneed to be conducted to assess the best candidate for hydraulic fracturing to maximizesuccess ratio. Even though a common practice, candidate-well selection is not astraightforward process and up to now, there has not been a well-defined approach toaddress this process. Conventional methods are not easy to use for nonlinear process,such as candidate-well selection that goes through a group of parameters having differentattributes and features such as geological aspect, reservoir and fluid characteristics,production details, etc. and that’s because it is difficult to describe properly all theirnonlinearities. In that matter, Artificial Intelligence approach is expected to be analternative solution for this condition.